5862 research outputs found
Sort by
Decoding Rhythmic Complexity: a Nonlinear Dynamics Approach via Visibility Graphs for Classifying Asymmetrical Rhythmic Frameworks of Turkish Classical Music
No full textThe non-isochronous, hierarchical rhythmic cycles (usuls) of Turkish Classical Music (TCM) exhibit emergent temporal structures that challenge conventional rhythm analysis based on metrical regularity. To address this challenge, this study presents a complexity-oriented framework for usul classification, grounded in nonlinear time series analysis and network-based representations. Rhythmic signals are processed through energy envelope extraction, diffusion entropy analysis, and spectral transformations to capture multiscale temporal dynamics. Visibility graphs (VGs) are constructed from these representations to encode underlying structural complexity and temporal dependencies. Features derived from VG adjacency matrices serve as complexity-sensitive descriptors and enable high-accuracy classification (0.99) across 40 usul classes and 628 compositions. Energy envelope-derived graphs provide the most discriminative information, highlighting the importance of amplitude modulation in encoding rhythmic structure. Beyond classification, the analysis reveals self-organizing patterns and signatures of complexity, such as quasi-periodicity, scale-dependent variability, and entropy saturation, suggesting that usuls function as adaptive, nonlinear systems rather than metrically constrained patterns. The topological features extracted from the resulting graphs align with theoretical constructs from complexity science, such as modularity and long-range temporal correlations. This positions usul as an exemplary case for studying structured temporal complexity in cultural artifacts through the lens of dynamical systems. These findings contribute to computational rhythm analysis by demonstrating the efficacy of complexity measures in characterizing culturally specific rhythmic systems.Science Citation Index Expande
Examining the Role of Dark and Light Triad Traits on Sociosexuality
No full textSociosexual orientation-the tendency toward casual sex, is associated with dispositional components of personality such as higher scores of Dark Triad traits (narcissism, Machiavellianism, and psychopathy). Yet, it remains unknown which specific Dark Triad traits and Light Triad traits (Kantianism, Humanism, and Faith in Humanity) predict sociosexuality and its dimensions while controlling for the others. In the current study, using an online community sample (N = 308), we examined the links between Dark Triad traits, Light Triad traits, overall sociosexuality, and sociosexuality dimensions (attitude, behavior, desire). Using hierarchical regression, we found that only psychopathy emerged as the predictor of behavior, desire, attitude dimensions, and sociosexuality total score. This effect held when controlling for age, sex, relationship status, the other two Dark personality traits, and the Light Triad. Results suggest that individuals high on psychopathy have a greater tendency toward uncommitted relationships.Fulbright and the Scientific and Technological Research Council of TurkeyThe present research was conducted during the fourth author's post-doctoral research fellowship funded by Fulbright and the Scientific and Technological Research Council of Turkey.Social Science Citation Inde
Robotization and Gender Role Attitudes: Evidence From 32 Countries
No full textArtan robot kullanımı, toplumsal cinsiyet normlarında olumlu değişikliklere yol açabilir mi? WVS (Dünya Değer Anketi), EVS (Avrupa Değer Anketi) ve Uluslararası Robotik Federasyonu'ndan 2004-2020 yılları arasında alınan verileri kullanarak robot maruziyetinin toplumsal cinsiyet rol tutumları ¨üzerindeki etkisine dair geniş ölçekli kanıtlar sunuyoruz. Bulgularımız, kadınların işgücüne katılım oranındaki artısın, belirli bir alanda cinsiyet yanlılığı olasılığının azaltma yönünde bir ile ilişkisi olduğunu göstermektedir. Eğitim alanındaki cinsiyet yanlılığı hariç, diğer alanlarda istatistiksel olarak kanıtlanabilir bir etkiden söz edilebilmektedir. Bireysel robot maruziyeti araçsal değişkenini kullanan analiz, kadın işgücü katılım oranındaki artışın ekonomik, politik, ev içi ve eğitim alanlarındaki toplumsal cinsiyet önyargılarını önemli ölçüde azalttığını gösteriyor. Bu bulgular, teknolojik değişikliklerin yalnızca işgücü piyasasını değil, aynı zamanda toplumsal normları da dönüştürme potansiyeline sahip olduğunu ortaya koyuyor. Çalışma, toplumsal cinsiyet eşitliğini artırmak için robotizasyonun etkilerinin daha derin bir şekilde anlaşılmasının ve buna göre politikaların şekillendirilmesinin önemine dikkat çekiyor.Could the increasing use of robots contribute to positive changes in gender social norms? We present large-scale evidence on the impact of robot exposure on gender social role attitudes using data from WVS (World Value Survey), EVS (European Value Survey), and the International Federation of Robotics from 2004 to 2020. Based on the feedback, the revised interpretation should focus on the likelihood of bias in a specific domain and explicitly address the implications of the results. Our findings suggest that an increase in female labor force participation is associated with a reduction in the likelihood of gender bias in a given field. In areas other than the gender bias domain related to education, a statistically provable effect can be mentioned. This reduction can be attributed to the indirect effects of individual exposure to robots, which appear to influence gender norms. The analysis using the individual robot exposure instrumental variable shows that an increase in female labor force participation rate significantly reduces gender biases in economic, political, domestic, and educational areas. These findings reveal that technological changes have the potential to transform not only the labor market but also social norms. The study draws attention to the importance of a deeper understanding of the effects of robotization and shaping policies accordingly in order to increase gender equality
A New a Flow-Based Approach for Enhancing Botnet Detection Using Convolutional Neural Network and Long Short-Term Memory
No full textHeidari, Arash/0000-0003-4279-8551Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method's propensity for delivering a promising performance in the face of these adversarial challenges.Qatar National LibraryOpen Access funding provided by the Qatar National Library.Science Citation Index Expande
Colonialism in Sub-Saharan Africa, Access To Finance, and Firm Growth
No full textWhether adequate access to external finance matters for firm-growth remains an unsettled debate in the finance literature, mainly because of endogeneity concerns. In this paper, we approach these concerns with two instruments constructed from colonial history that plausibly explain the current variations in financial development across sub-Saharan African (SSA) economies. We conjecture that these instruments-- the firm's distance from a colonial railway station and whether it is located in an area that had colonial settlements-provide potential channels of impact that identify the present-day effects of access to finance on firm-growth across SSA. By using these instruments, empirical results underscore the primacy of access to finance in firmgrowth and consistently suggest that firms with access to finance are more likely to experience higher revenue growth and asset growth. Overall, our results are consistent and robust to alternative specifications and highlight the importance of access to finance for firms. Our findings provide policy implications on the development of the banking sector as well as private sector development.Social Science Citation Inde
Performance Evaluation of Operators in the Telecommunication Industry
No full textOne of the pioneer technological developments for society is telecommunication. Thus, related industry is proliferating worldwide, and this acceleration forces companies to constantly increase their service quality and product variety to attract new customers. Especially in regions with high growth, losing customers to competitors (s) causes considerable costs in the long run. Therefore, companies should constantly test their products and increase service quality to continue customer loyalty and help society communicate better. This study considers almost all scenarios a customer can encounter, from the first step of being a customer to canceling the contract. Three telecommunication service operators in Turkiye are reviewed based on 15 criteria. First, the “hesitant fuzzy Analytic Hierarchy Process” (HF-AHP) is employed to compute the importance weights of criteria. Then, the telecommunication companies are assessed via hesitant fuzzy “VIsekriterijumska optimizacija i KOmpromisno Resenje” (HF-VIKOR), considering these criteria’s weights. HF-TOPSIS is also applied as a comparative analysis to validate the study’s outcomes. Results provide valuable outcomes and policies for the decision-makers in the telecommunication industry. © The Author(s) 2025.Science Citation Index Expande
The Influence of Eye Gaze Interaction Technique Expertise and the Guided Evaluation Method on Text Entry Performance Evaluations
No full textAny investigation of learning unfamiliar text entry systems is affected by the need to train participants on multiple new components simultaneously, such as novel interaction techniques and layouts. The Guided Evaluation Method (GEM) addresses this challenge by bypassing the need to learn layout-specific skills for text entry. However, a gap remains as the GEM's performance has not been assessed in situations where users are unfamiliar with the interaction technique involved, here eye-gaze-based dwell. To address this, we trained participants on only the eye-gaze-based interaction technique over eight days with QWERTY and then evaluated their performance on the OPTI layout with the GEM. Results showed that the unfamiliar OPTI layout outperformed QWERTY, with QWERTY's speed aligning with previous findings, suggesting that interaction technique expertise significantly impacts performance outcomes. Importantly, we also identified that for scenarios where the familiarity with the involved interaction technique(s) is the same, the GEM analyzes the performance of keyboard layouts effectively and quickly identifies the best option
Cognitive Styles and Behavioral Systems: Linking Looming Cognitive Style and Reinforcement Sensitivity
No full textGokdag, Ceren/0000-0002-9111-2811Background: Looming cognitive style, with its social and physical subtypes, is highly influential on how individuals perceive and respond to threats. Despite its robust relationship with anxiety, its relationship with other traits is underexplored. Revised reward sensitivity theory also addresses individual differences in approach, avoidance, and susceptibility to fear and anxiety. The current study examined associations of behavioral activation (BAS), inhibition (BIS), and fight-flight-freeze systems (FFFS) with social and physical looming. Method: Data were collected online from 401 adults (343 women) between the ages 18 and 65 (M = 22.78 (SD = 6.57) using measures of looming cognitive style, reinforcement sensitivity, anxiety, and depression. Results: The findings showed that social and physical looming were positively associated with BIS and FFFS, controlling for age, gender, and anxiety and depression symptoms. Additionally, social looming was negatively associated with BAS. Conclusions: The findings indicate that social and physical looming are linked to heightened sensitivity to threat and, in the case of social looming, reduced reward sensitivity. These results underscore the role of looming cognitive style in shaping anxiety-related behaviors and responses to environmental stimuli.Social Science Citation Inde
Machine Learning Applications on Purchase Prediction for E-commerce Marketplaces
No full textBu araştırma, bir e-ticaret kullanıcısının oturum sonunda satın alma yapıp yapmayacağını tıklama akışı verilerini kullanarak tahmin edebilen bir makine öğrenimi çerçevesi önermektedir. Çalışma, son kullanıcı eylemlerinin düzleştirilmiş dizileri, oturum bazlı istatistikler ve her ikisini entegre eden yenilikçi bir hibrit model dahil olmak üzere çeşitli veri temsillerini incelemektedir. Mevcut literatür genellikle tek bir veri temsilini ele alırken, bu araştırma oturum bazlı veriler ile kullanıcı eylemlerinin potansiyel sinerjisini kapsamlı bir şekilde değerlendirmektedir. Önerilen metodoloji, LightGBM'i temel tahmin modeli olarak kullanmaktadır. Ayrıca, karar ağaçları, gradyan artırma, rastgele ormanlar ve lojistik regresyon gibi algoritmalar doğrulama amacıyla uygulanmıştır. Öznitelik önem analizi, satın alma olasılığının temel belirleyicileri olarak son kullanıcı eyleminden bu yana geçen süre, oturum süresi ve belirli ürün etkileşimlerini öne çıkarmaktadır. Bu çalışma, ağaç tabanlı bir tahmin modeli içinde hibrit veri temsillerinin pratik faydasını göstererek, gerçek zamanlı satın alma tahmini için ölçeklenebilir ve yorumlanabilir bir çerçeve sunmaktadır. Bulgularımız, e-ticaret platformlarının satın alma tahminlerini iyileştirmesine ve pazarlama stratejilerini optimize etmesine yönelik uygulanabilir içgörüler sağlamaktadır.This research proposes a machine learning framework that can accurately predict whether a user will purchase at the end of a session in e-commerce using clickstream data. The study explores various data representations, including flattened sequences of recent user actions, aggregated session statistics, and a novel hybrid model integrating both. While existing literature often explores a single data representation, this research comprehensively examines the potential synergies between aggregated session-level data and recent user actions. The proposed methodology employs LightGBM as the core predictive model. Algorithms such as decision trees, gradient boosting, random forests, and logistic regression were employed for validation. Feature importance analysis highlights key determinants of purchase likelihood, including time since the last user action, session duration, and specific product interactions. By demonstrating the practical utility of hybrid data representations within a tree-based predictive model, this study introduces a scalable and interpretable framework for real-time purchase prediction. Our findings offer a scalable and interpretable framework for e-commerce platforms to enhance purchase predictions and optimize marketing strategies
Feedback-Based Quantum Algorithm for Constrained Optimization Problems
No full textThe feedback-based algorithm for quantum optimization (FALQON) has recently been proposed to find ground states of Hamiltonians and solve quadratic unconstrained binary optimization problems. This paper efficiently generalizes FALQON to tackle quadratic constrained binary optimization (QCBO) problems. For this purpose, we introduce a new operator that encodes the problem's solution as its ground state. Using control theory, we design a quantum control system such that the state converges to the ground state of this operator. When applied to the QCBO problem, we show that our proposed algorithm saves computational resources by reducing the depth of the quantum circuit and can perform better than FALQON. The effectiveness of our proposed algorithm is further illustrated through numerical simulations.Independent Research Fund Denmark (DFF) [0136-00204B]This work was supported by Independent Research Fund Denmark (DFF), project number 0136-00204B.Conference Proceedings Citation Index - Scienc